Analytical and Bioanalytical Chemistry

, Volume 405, Issue 15, pp 5147–5157 | Cite as

Characterising and correcting batch variation in an automated direct infusion mass spectrometry (DIMS) metabolomics workflow

  • J. A. Kirwan
  • D. I. Broadhurst
  • R. L. Davidson
  • M. R. ViantEmail author
Original Paper


Direct infusion mass spectrometry (DIMS)-based untargeted metabolomics measures many hundreds of metabolites in a single experiment. While every effort is made to reduce within-experiment analytical variation in untargeted metabolomics, unavoidable sources of measurement error are introduced. This is particularly true for large-scale multi-batch experiments, necessitating the development of robust workflows that minimise batch-to-batch variation. Here, we conducted a purpose-designed, eight-batch DIMS metabolomics study using nanoelectrospray (nESI) Fourier transform ion cyclotron resonance mass spectrometric analyses of mammalian heart extracts. First, we characterised the intrinsic analytical variation of this approach to determine whether our existing workflows are fit for purpose when applied to a multi-batch investigation. Batch-to-batch variation was readily observed across the 7-day experiment, both in terms of its absolute measurement using quality control (QC) and biological replicate samples, as well as its adverse impact on our ability to discover significant metabolic information within the data. Subsequently, we developed and implemented a computational workflow that includes total-ion-current filtering, QC-robust spline batch correction and spectral cleaning, and provide conclusive evidence that this workflow reduces analytical variation and increases the proportion of significant peaks. We report an overall analytical precision of 15.9 %, measured as the median relative standard deviation (RSD) for the technical replicates of the biological samples, across eight batches and 7 days of measurements. When compared against the FDA guidelines for biomarker studies, which specify an RSD of <20 % as an acceptable level of precision, we conclude that our new workflows are fit for purpose for large-scale, high-throughput nESI DIMS metabolomics studies.


Batch effect Block effects QC-RSC Relative standard deviation Reproducibility 



This work was in part supported by the UK Natural Environmental Research Council (NERC) Biomolecular Analysis Facility at the University of Birmingham (R8-H10-61) and by the British Heart Foundation. The FT-ICR used in this research was obtained through the Birmingham Science City Translational Medicine: Experimental Medicine Network of Excellence project, with support from Advantage West Midlands (AWM). David Broadhurst holds salary support from Pfizer Canada. We thank Leansale Ltd. Abattoir, Birmingham, for donating the hearts and Tristan Payne for providing MATLAB programming support.

Supplementary material

216_2013_6856_MOESM1_ESM.pdf (2.6 mb)
ESM 1 (PDF 2643 kb)


  1. 1.
    De Vos RCH, Moco S, Lommen A, Keurentjes JJB, Bino RJ, Hall RD (2007) Untargeted large-scale plant metabolomics using liquid chromatography coupled to mass spectrometry. Nat Protoc 2(4):778–791CrossRefGoogle Scholar
  2. 2.
    Bijlsma S, Bobeldijk L, Verheij ER, Ramaker R, Kochhar S, Macdonald IA, van Ommen B, Smilde AK (2006) Large-scale human metabolomics studies: a strategy for data (pre-) processing and validation. Anal Chem 78(2):567–574CrossRefGoogle Scholar
  3. 3.
    Pinto RC, Gerber L, Eliasson M, Sundberg B, Trygg J (2012) Strategy for minimizing between-study variation of large scale phenotypic experiments using multivariate analysis. Anal Chem 84(20):8675–8681CrossRefGoogle Scholar
  4. 4.
    Begley P, Francis-McIntyre S, Dunn WB, Broadhurst DI, Halsall A, Tseng A, Knowles J, Goodacre R, Kell DB (2009) Development and performance of a gas chromatography-time-of-flight mass spectrometry analysis for large-scale nontargeted metabolomic studies of human serum. Anal Chem 81(16):7038CrossRefGoogle Scholar
  5. 5.
    Saini A (2012) London’s Olympic drug testing lab to become national phenome center. Science 337(6094):513–513CrossRefGoogle Scholar
  6. 6.
    Zelena E, Dunn WB, Broadhurst D, Francis-McIntyre S, Carroll KM, Begley P, O’Hagan S, Knowles JD, Halsall A, Wilson ID, Kell DB (2009) Development of a robust and repeatable UPLC-MS method for the long-term metabolomic study of human serum. Anal Chem 81(4):1357–1364CrossRefGoogle Scholar
  7. 7.
    Sumner LW, Amberg A, Barrett D, Beale MH, Beger R, Daykin CA, Fan TWM, Fiehn O, Goodacre R, Griffin JL, Hankemeier T, Hardy N, Harnly J, Higashi R, Kopka J, Lane AN, Lindon JC, Marriott P, Nicholls AW, Reily MD, Thaden JJ, Viant MR (2007) Proposed minimum reporting standards for chemical analysis. Metabolomics 3(3):211–221CrossRefGoogle Scholar
  8. 8.
    Steinbeck C, Conesa P, Haug K, Mahendraker T, Williams M, Maguire E, Rocca-Serra P, Sansone SA, Salek RM, Griffin JL (2012) MetaboLights: towards a new COSMOS of metabolomics data management. Metabolomics 8(5):757–760CrossRefGoogle Scholar
  9. 9.
    Draisma HHM, Reijmers TH, van der Kloet F, Bobeldijk-Pastorova I, Spies-Faber E, Vogels JTWE, Meulman JJ, Boomsma DI, van der Greef J, Hankemeier T (2010) Equating, or correction for between-block effects with application to body fluid LC–MS and NMR metabolomics data sets. Anal Chem 82(3):1039–1046CrossRefGoogle Scholar
  10. 10.
    Sangster TP, Wingate JE, Burton L, Teichert F, Wilson ID (2007) Investigation of analytical variation in metabonomic analysis using liquid chromatography/mass spectrometry. Rapid Commun Mass Spectrom 21(18):2965–2970CrossRefGoogle Scholar
  11. 11.
    Dunn WB, Brown M, Worton SA, Davies K, Jones RL, Kell DB, Heazell AEP (2012) The metabolome of human placental tissue: investigation of first trimester tissue and changes related to preeclampsia in late pregnancy. Metabolomics 8(4):579–597CrossRefGoogle Scholar
  12. 12.
    Dunn WB, Broadhurst D, Begley P, Zelena E, Francis-McIntyre S, Anderson N, Brown M, Knowles JD, Halsall A, Haselden JN (2011) Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat Protoc 6(7):1060–1083CrossRefGoogle Scholar
  13. 13.
    Bolstad BM, Irizarry RA, Åstrand M, Speed TP (2003) A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19(2):185–193CrossRefGoogle Scholar
  14. 14.
    Ballman KV, Grill DE, Oberg AL, Therneau TM (2004) Faster cyclic loess: normalizing RNA arrays via linear models. Bioinformatics 20(16):2778–2786CrossRefGoogle Scholar
  15. 15.
    Dudoit S, Yang YH, Callow MJ, Speed TP (2002) Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Stat Sin 12(1):111–140Google Scholar
  16. 16.
    Veselkov KA, Vingara LK, Masson P, Robinette SL, Want E, Li JV, Barton RH, Boursier-Neyret C, Walther B, Ebbels TM (2011) Optimized preprocessing of ultra-performance liquid chromatography/mass spectrometry urinary metabolic profiles for improved information recovery. Anal Chem 83(15):5864–5872CrossRefGoogle Scholar
  17. 17.
    Xia J, Mandal R, Sinelnikov IV, Broadhurst D, Wishart DS (2012) MetaboAnalyst 2.0—a comprehensive server for metabolomic data analysis. Nucleic Acids Res 40(W1):W127–W133CrossRefGoogle Scholar
  18. 18.
    Kamleh MA, Ebbels TMD, Spagou K, Masson P, Want EJ (2012) Optimizing the use of quality control samples for signal drift correction in large-scale urine metabolic profiling studies. Anal Chem 84(6):2670–2677CrossRefGoogle Scholar
  19. 19.
    van der Kloet FM, Bobeldijk I, Verheij ER, Jellema RH (2009) Analytical error reduction using single point calibration for accurate and precise metabolomic phenotyping. J Proteome Res 8(11):5132–5141CrossRefGoogle Scholar
  20. 20.
    Taylor NS, Weber RJM, Southam AD, Payne TG, Hrydziuszko O, Arvanitis TN, Viant MR (2009) A new approach to toxicity testing in Daphnia magna: application of high throughput FT-ICR mass spectrometry metabolomics. Metabolomics 5(1):44–58CrossRefGoogle Scholar
  21. 21.
    Southam AD, Payne TG, Cooper HJ, Arvanitis TN, Viant MR (2007) Dynamic range and mass accuracy of wide-scan direct infusion nanoelectrospray Fourier transform ion cyclotron resonance mass spectrometry-based metabolomics increased by the spectral stitching method. Anal Chem 79(12):4595–4602CrossRefGoogle Scholar
  22. 22.
    Giavalisco P, Hummel J, Lisec J, Inostroza AC, Catchpole G, Willmitzer L (2008) High-resolution direct infusion-based mass spectrometry in combination with whole 13C metabolome isotope labeling allows unambiguous assignment of chemical sum formulas. Anal Chem 80(24):9417–9425CrossRefGoogle Scholar
  23. 23.
    Wei X, Sun W, Shi X, Koo I, Wang B, Zhang J, Yin X, Tang Y, Bogdanov B, Kim S (2011) MetSign: a computational platform for high-resolution mass spectrometry-based metabolomics. Anal Chem 83(20):7668–7675CrossRefGoogle Scholar
  24. 24.
    Aliferis KA, Jabaji S (2012) FT-ICR/MS and GC-EI/MS metabolomics networking unravels global potato sprout’s responses to Rhizoctonia solani infection. PLoS One 7(8):e42576CrossRefGoogle Scholar
  25. 25.
    Draper J, Lloyd AJ, Goodacre R, Beckmann M (2012) Flow infusion electrospray ionisation mass spectrometry for high throughput, non-targeted metabolite fingerprinting: a review. Metabolomics:1–26. doi: 10.1007/s11306-012-0449-x
  26. 26.
    Wu H, Southam AD, Hines A, Viant MR (2008) High throughput tissue extraction protocol for NMR- and MS-based metabolomics. Anal Biochem 372(2):204–212CrossRefGoogle Scholar
  27. 27.
    Weber RJM, Southam AD, Sommer U, Viant MR (2011) Characterization of isotopic abundance measurements in high resolution FT-ICR and orbitrap mass spectra for improved confidence of metabolite identification. Anal Chem 83(10):3737–3743CrossRefGoogle Scholar
  28. 28.
    Payne TG, Southam AD, Arvanitis TN, Viant MR (2009) A signal filtering method for improved quantification and noise discrimination in Fourier transform ion cyclotron resonance mass spectrometry-based metabolomics data. J Am Soc Mass Spectrom 20(6):1087–1095. doi: 10.1016/j.jasms.2009.02.001 CrossRefGoogle Scholar
  29. 29.
    Rubingh CM, Bijlsma S, Jellema RH, Overkamp KM, van der Werf MJ, Smilde AK (2009) Analyzing longitudinal microbial metabolomics data. J Proteome Res 8(9):4319–4327CrossRefGoogle Scholar
  30. 30.
    Smilde AK, van der Werf MJ, Bijlsma S, van der Werff-van BJC, Jellema RH (2005) Fusion of mass spectrometry-based metabolomics data. Anal Chem 77(20):6729–6736CrossRefGoogle Scholar
  31. 31.
    Dieterle F, Ross A, Schlotterbeck G, Senn H (2006) Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics. Anal Chem 78(13):4281–4290CrossRefGoogle Scholar
  32. 32.
    Parsons HM, Ekman DR, Collette TW, Viant MR (2009) Spectral relative standard deviation: a practical benchmark in metabolomics. Analyst 134(3):478–485CrossRefGoogle Scholar
  33. 33.
    Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate—a practical and powerful approach to multiple testing. J Royal Stat Soc Ser B-Methodol 57(1):289–300Google Scholar
  34. 34.
    Hrydziuszko O, Viant MR (2012) Missing values in mass spectrometry based metabolomics: an undervalued step in the data processing pipeline. Metabolomics 8:S161–S174CrossRefGoogle Scholar
  35. 35.
    Parsons HM, Ludwig C, Gunther UL, Viant MR (2007) Improved classification accuracy in 1- and 2-dimensional NMR metabolomics data using the variance stabilising generalised logarithm transformation. BMC Bioinforma 8:234. doi: 10.1186/1471-2105-8-234 CrossRefGoogle Scholar
  36. 36.
    de Boor C (1978) A practical guide to splines. Springer, New YorkCrossRefGoogle Scholar
  37. 37.
    Evans AM, DeHaven CD, Barrett T, Mitchell M, Milgram E (2009) Integrated, nontargeted ultrahigh performance liquid chromatography/electrospray ionization tandem mass spectrometry platform for the identification and relative quantification of the small-molecule complement of biological systems. Anal Chem 81(16):6656–6667CrossRefGoogle Scholar
  38. 38.
    Saylor PJ, Karoly ED, Smith MR (2012) Prospective study of changes in the metabolomic profiles of men during their first three months of androgen deprivation therapy for prostate cancer. Clin Cancer Res 18(13):3677–3685CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • J. A. Kirwan
    • 1
  • D. I. Broadhurst
    • 2
  • R. L. Davidson
    • 3
  • M. R. Viant
    • 1
    • 3
    Email author
  1. 1.School of BiosciencesUniversity of BirminghamBirminghamUK
  2. 2.Department of MedicineUniversity of AlbertaEdmontonCanada
  3. 3.NERC Biomolecular Analysis Facility—Metabolomics Node (NBAF-B)University of BirminghamBirminghamUK

Personalised recommendations